Click on a year to read the news stories
Papers accepted to COLING 2018:
- Automated Fact Checking: Task Formulations, Methods and Future Directions. James Thorne and Andreas Vlachos.
- Topic or Style? Exploring the Most Useful Features for Authorship Attribution. Yunita Sari, Mark Stevenson and Andreas Vlachos.
- deepQuest: A Framework for Neural-based Quality Estimation. Julia Ive, Frédéric Blain and Lucia Specia.
- Can Rumour Stance Alone Predict Veracity? – Sebastian Dungs, Ahmet Aker, Norbert Fuhr and Kalina Bontcheva.
- Professor Jochen Leidner has become a member of the Industrial Liaison Board
- Conference paper: Igbo Diacritic Restoration using Embedding Models. Ignatius Ezeani, Mark Hepple, Ikechukwu Onyenwe and Enemuo Chioma
- Andreas Vlachos gave a talk at the University of Sussex on Imitation Learning, Zero-shot Learning and Automated Fact Checking
- Andreas Vlachos gave a talk on Artificial Intelligence vs Misinformation with James Thorne at the Pint of Science Launch Festival in Sheffield
- FEVER shared task on fact checking claims against Wikipedia using the 200K claims dataset described in our upcoming NAACL paper started!
- Andreas Vlachos gave a talk at Benevolent.AI on automated fact checking
- Andreas Vlachos gave a talk at the Ubiquitous Knowledge Processing at Darmstadt on automated fact checking and imitation learning
Papers accepted to EACL 2017:
- Continuous N-gram Representations for Authorship Attribution, Y. Sari, A. Vlachos, M. Stevenson, Proceedings of EACL: Volume 2, Short Papers pdf bib
- An Extensible Framework for Verification of Numerical Claims, J. Thorne, A. Vlachos, Proceedings of the Software Demonstrations pdf bib
- Book: Natural Language Processing for the Semantic Web, Diana Maynard, Kalina Bontcheva, Isabelle Augenstein. Morgan and Claypool, December 2016. ISBN:97816270590
- Journal paper: A Framework for Real-time Semantic Social Media Analysis. Diana Maynard, Ian Roberts, Mark A. Greenwood, Dominic Rout and Kalina Bontcheva. Web Semantics: Science, Services and Agents on the World Wide Web, 2017
- Conference paper: Towards an Infrastructure for Understanding and Interlinking Knowledge Co-Creation in European research, Diana Maynard, Adam Funk and Benedetto Lepori. ESWC 2017 Workshop on Scientometrics, Portoroz, Slovenia, May 2017
- Diana Maynard taught 2 practical tutorials at the AI Seminar on Social Media Content Analysis, UPC Barcelona, May 2017
- Diana Maynard gave an invited tutorial at the EU CLARIN-PLUS workshop on "Creation and Use of Social Media Resources", Lithuania, 2017
- Diana Maynard gave an invited talk at 2017 Joint EC-OECD workshop on Semantic Technologies and Semantic Web: Structuring Data for STI Policy Analysis, 19 June, Brussels
- Diana Maynard gave an invited talk at 2017 EPSRC The Future of Patent Analytics Workshop, 3 March, Cambridge, UK
- The KNOWMAK project has started. A 3 year EC H2020 project from 1 Jan'17 - 31 Dec’20. The University of Sheffield PI is Diana Maynard.
- Diana Maynard was Programme Chair of the ESWC conference in Portoroz, Slovenia in May.
- Diana Maynard has won an ESRC-funded award from Understanding Society to access and analyse EU Referendum UK household survey data, for the project "Brexit narratives of place and scale: a media environment analysis of the EU Referendum debate” Co-PIs: Jackie Harrison (Journalism), J. Miguel Kanai (Geography)
Papers accepted for COLING 2016:
- Representation and Learning of Temporal Relations. L. Derczynski (2016). COLING
- Broad Twitter Corpus: A Diverse Named Entity Recognition Resource. L. Derczynski, K. Bontcheva, I. Roberts (2016). COLING
- Stance classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations. A. Zubiaga, E. Kochkina, M. Liakata, R. Procter, M. Lukasik. (2016). COLING
- Anita: An Intelligent Text Adaptation Tool. G. Paetzold, L. Specia. (2016). COLING
- Understanding the Lexical Simplification Needs of non-Native Speakers of English. G. Paetzold, L. Specia. (2016). COLING
- Collecting and Exploring Everyday Language for Predicting Psycholinguistic Properties of Words. G. Paetzold, L. Specia. (2016). COLING
- Imitation learning for language generation from unaligned data. G. Lampouras, A. Vlachos. (2016). COLING
- Carolina Scarton, Gustavo Paetzold and Lucia Specia will give a tutorial at COLING 2016, titledQuality estimation for language output applications
- We are please to announce that Gutsavo Paetzold has passed his PhD viva, having submitted only 2 years after joining as a PhD student.
- Leon Derczynski will give a course at ESSLLI 2017 with Matteo Magnani, titled "Networks and User-generated Content"
- Book in press in Springer Studies in Computational Intelligence: Automatically ordering events and times in text - L Derczynski
- Diana Maynard has had an article on automatic sarcasm detection published in Quartz Magazine
- Diana Maynard will give tutorials on NLP and Social Media Analysis at the 1st International Deep Learning, Big Data and Big Compute Camp, Rabat, Morocco, 24-28 October 2016. https://dlwensias.wordpress.com/2016/09/05/3/
- Paper published in European Psychiatry: Novel psychoactive substances: an investigation of temporal trends in social media and electronic health records - A Kolliakou, M Ball, L Derczynski, D Chandran, G Gkotsis, P Deluca, R Jackson, H Shetty, R Stewart
- Mark Stevenson and Adam Poulson are collaborating with ScHaRR and Human on a project to visualise emotion in social media at the Festival of the Mind - Link to the Guardian Article
- Paper: An IR-based Approach Utilising Query Expansion for Plagiarism Detection in MEDLINE. R. Nawab, M Stevenson and P. Clough (2016). IEEE/ACM Transactions of Computational Biology and Bioinformatics.
- Paper: The Effect of Word Sense Disambiguation Accuracy on Literature Based Discovery. J. Preiss and M. Stevenson (2016). BMC Decision Making and Medical Informatics.
- Paper: A Corpus of Potentially Contradictory Research Claims from Cardiovascular Research Abstracts. A. Alamri and M. Stevenson (2016). Journal of Biomedical Semantics, 7 (36).
Papers accepted for EMNLP 2016:
- Stance Detection with Bidirectional Conditional Encoding , Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos and Kalina Bontcheva
- Leon Derczynski has won an NVIDA hardware grant for summary generation from collections of text.
- Prof. Lucia Specia has been awarded an EC H2020 funded ERC Starting Grant. The project on Multimodal Context Modelling for Machine Translation (MultiMT) will start on 1 July 2016 for 5 years.
Papers accepted for ACL 2016
- Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter. Michal Lukasik, P. K. Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, Trevor Cohn.
- Metrics for Evaluation of Word-level Machine Translation Quality Estimation. Varvara Logacheva, Michal Lukasik and Lucia Specia.
Papers accepted for TSD2016:
- Automatic Restoration of Diacritics for Igbo Language . Ignatius Ezeani, Mark Hepple and Ikechukwu Onyenwe.
- Predicting Morphologically-Complex Unknown Words in Igbo. Ikechukwu Onyenwe and Mark Hepple
- Paper nominated for Best Paper Award at WebSci 2016: Miriam Fernandez, Harith Alani, Lara Piccolo, Christoph Meili, Diana Maynard and Meia Wippoo. Talking Climate Change via Social Media: Communication, Engagement and Behaviour, May 22-25 2016, Hannover, Germany.
- Diana Maynard taught a 3-hour practical tutorial at the AI Seminar on Social Media Content Analysis, UPC Barcelona, 9-13 May 2016.
- Leon Derczynski is co-organising a workshop on Noisy User-generated Text (WNUT) at COLING in Osaka, Japan, 10th December 2016.
- Diana Maynard will teach two 6-hour courses, "Introduction to NLP" and "Practical social media and sentiment analysis" at the University of Essex Big Data and Analytics Summer School in September 2016. http://www.essex.ac.uk/iads/events/summer-school.aspx
- Andreas Vlachos will be speaking at the Lisbon Machine Learning Summer School about imitation learning for structured prediction.
- Andreas Vlachos will be speaking at the Knowledge Representation Workshop at the University of Liverpool on 28th June 2016.
- Paper: Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing. James Goodman, Andreas Vlachos and Jason Naradowsky. ACL 2016.
- Paper: Emergent: A novel data-set for stance classification. William Ferreira and Andreas Vlachos. NAACL 2016.
- Paper: Large-scale Multitask Learning for Machine Translation Quality Estimation . Kashif Shah and Lucia Specia. NAACL 2016.
- Paper: Phrase Level Segmentation and Labelling of Machine Translation Errors. Frederic Blain, Varvara Logacheva, and Lucia Specia. In Proc. of Language Resources and Evaluation Conference (LREC), May 2016, Portoroz, Slovenia
- Paper: Challenges of Evaluating Sentiment Analysis Tools on Social Media. Diana Maynard and Kalina Bontcheva. In Proc. of Language Resources and Evaluation Conference (LREC), May 2016, Portoroz, Slovenia
- Paper: Complementarity, F-score, and NLP Evaluation. Leon Derczynski. In Proc. of Language Resources and Evaluation Conference (LREC), May 2016, Portoroz, Slovenia
- Paper: GATE-Time: Extraction of Temporal Expressions and Events Leon Derczynski, Jannik Strötgen, Diana Maynard, Mark A. Greenwood, Manuel Jung. In Proc. of Language Resources and Evaluation Conference (LREC), May 2016, Portoroz, Slovenia
- Dr. Diana Maynard has been awarded a grant for a fully-funded 4-year PhD student project by the Grantham Centre for Sustainable Futures, to start in October 2016, on the topic of disaster relief reporting and climate change. The Grantham Scholar will be supervised by Diana Maynard and co-supervised by Prof. Jacqueline Harrison from the Dept of Journalism and Prof. Shaun Quegan from the Centre for Terrestrial Carbon Dynamics.
- The next annual GATE training course will be held from 6-10 June 2016.
- Mark Stevenson was awarded a grant from Defence Science and Technology Laboratory: "Hypothesis Generation and Visualisation from Data"
- Paper: A Graph-based Approach to Topic Clustering for Online News. Ahmet Aker, Emina Kurtic, Balamurali Andiyakkal Rajendran, Monica Paramita, Emma Barker, Mark Hepple and Rob Gaizauskas. ECIR 2016.
- Paper: Automated Content Analysis: A Sentiment Analysis on Malaysian Government Social Media. Siti Salwa Hasbullah and Diana Maynard. In Proc. of ACM International Conference on Ubiquitous Information Management and Communication (IMCOM), January 2016, Danang, Vietnam.
- The COMRADES project has started. A 3 year EC H2020 project from 1 Jan'16 - 31 Dec'18. The University of Sheffield PI is Prof. Kalina Bontcheva
- We are pleased to announce two new NLP Professors: Kalina Bontcheva and Lucia Specia have both been promoted to Personal Chair.
- A piece was published in the Guardian technology blog on Tuesday 8.12.2015 on our work in the EU-funded SENSEI project.
- Tutorial given by Diana Maynard at Search Solutions 2015, British Computer Society, London, November 2015: "Text analysis with GATE"
- Mark Stevenson is co-organising a workshop on Topic Models: Post-processing and Applications at CIKM 2015 with Nikolaos Aletras (UCL), Jey Han Lau (King's College London) and Timothy Baldwin (University of Melbourne).
- Andrés Duque from UNED in Madrid visited the group for 3 months (October - December 2015)
- Paper: Understanding climate change tweets: an open source toolkit for social media analysis. D. Maynard and K. Bontcheva. In Proc. of EnviroInfo 2015, Copenhagen, Sep. 2015.PDF
- Poster: Real-time Social Media Analytics through Semantic Annotation and Linked Open Data. D. Maynard, M. A. Greenwood, I. Roberts, G. Windsor, K. Bontcheva. Proceedings of WebSci 2015, Oxford, UK
- Paper: "Generalised Brown Clustering and Roll-Up Feature Generation". Leon Derczynski, Sean Chester. AAAI 2016.
- We are pleased to announce that Dr. Andreas Vlachos has joined the group from 1 September 2015.
- Paper: Evaluating Topic Representations for Exploring Document Collections. N. Aletras, T. Baldwin, J. Lau and M. Stevenson (to appear), Journal of the Association for Information Science and Technology
- Paper: Exploring Relation Types for Literature-based Discovery. J. Preiss, M. Stevenson and R. Gaizauskas. (to appear), Journal of the American Medical Informatics Association.
- Paper: Why are these similar? Investigating item similarity types in a large Digital Library. A. Gonzalez-Agirre, N. Aletras, G. Rigau, M. Stevenson and E. Agirre. (to appear), Journal of the Association for Information Science and Technology
- Paper: Cognitive Styles within an Exploratory Search System for Digital Libraries. P. Goodale, P. Clough, S. Fernando, N. Ford and M. Stevenson (2014), Journal of Documentation, 70(6):970-996.
- Paper: Improving Distant Supervision using Inference Learning. R. Roller, E. Agirre, A. Soroa and M. Stevenson (2015). In Proceedings of the 53rd Annual Meeting of the Association for Computational Lingusitics and the 7th International Conference on Natural Language Processing of the Asican Federation of Natural Language Processing (ACL-IJCNLP 2015), Beijing, China.
- Paper: A Hybrid Distributional and Knowledge-based Model of Lexical Semantics. N. Aletras and M. Stevenson (2015). In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, pages 20--29, Denver, Colorado
- Paper: Investigating Continuous Space Language Models for Machine Translation Quality Estimation. Kashif Shah, Raymond W. M. Ng, Fethi Bougares and Lucia Specia. EMNLP, 2015 (To Appear)
- Paper: SHEF-NN: Translation Quality Estimation with Neural Networks. Kashif Shah, Varvara Logacheva, Gustavo Paetzold, Frédéric Blain, Daniel Beck, Fethi Bougares and Lucia Specia. WMT, 2015 (To Appear)
- Paper: A study on the stability and effectiveness of features in quality estimation for spoken language translation. Raymond W. M. Ng, Kashif Shah, Lucia Specia and Thomas Hain. Interspeech, 2015.
- Paper: Quality estimation for ASR K-best list rescoring in spoken language translation. Raymond W. M. Ng, Kashif Shah, Wilker Aziz, Lucia Specia and Thomas Hain. ICASSP, 2015.
- Article: A Bayesian non-linear method for feature selection in machine translation quality estimation Kashif Shah, Trevor Cohn and Lucia Specia. Journal of Machine Translation, 2015.
- The Pheme project is co-supporting Clinical TempEval again in 2016, a shared evaluation task with the NIH THYME project and Harvard Children's Hospital, which will run at SemEval.
- Special issue on "Time and Information Retrieval" in the Information Processing & Management journal was published, with Leon Derczynski as lead guest editor.
- Martin Leginus from Aalborg University, co-supervised by Leon Derczynski, won the Best Student Paper award at WEBIST with his work improving tag clouds using entity disambiguation in streams.
- Sean Chester from Aarhus University will visit and give a seminar in late September.
- Book deal signed with O'Reilly on Temporal Information Processing for Language, by Leon Derczynski working with James Pustejovsky and Marc Verhagen (both from Brandeis).
- Our entry in the W-NUT entity recognition challenge in tweets won 3rd place for untyped entity recognition.
- Paper: Extracting Relations Between Non-Standard Entities using Distant Supervision and Imitation Learning.Isabelle Augenstein, Andreas Vlachos, Diana Maynard. EMNLP 2015.
- Article: Distantly Supervised Web Relation Extraction for Knowledge Base Population. Isabelle Augenstein, Diana Maynard, Fabio Ciravegna. Semantic Web Journal.
- Tutorial with Barry Norton at ESWC Summer School 2015: "Information Extraction with Linked Data"
- Article from the group published in the journal Information Processing and Management: Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, Kalina Bontcheva. 2015. Analysis of Named Entity Recognition and Linking for Tweets.
- Paper presented at the SemEval workshop: Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky, Marc Verhagen. 2015. SemEval-2015 Task 6: Clinical TempEval.
- Paper presented at the SemEval workshop: Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak. 2015. Swiss-Chocolate: Combining Flipout Regularization and Random Forest with Artificially Built Subsystems to Boost Text-Classification for Sentiment.
- Paper from the group presented at the SemEval workshop: Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski, Marcos Didonet del Fabro. 2015. UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval.
- Book chapter form the group to appear in The Handbook of Linguistic Annotation (edited by Nancy Ide and James Pustejovsky): Kalina Bontcheva, Leon Derczynski, Ian Roberts. 2015. Crowdsourcing Named Entity Recognition and Entity Linking Corpora.
- Paper from the group presented at the ISA-11 workshop: Hegler Tissot, Angus Roberts, Leon Derczynski, Genevieve Gorrell, Marcos Didonet del Fabro. 2015. Analysis of Temporal Expressions Annotated in Clinical Notes.
- Paper presented at the WEBIST conference: Martin Leginus, Leon Derczynski, Peter Dolog. 2015. Enhanced Information Access to Social Streams through Word Clouds with Entity Grouping.
- Paper from the group at the W-NUT workshop: Leon Derczynski, Isabelle Augenstein, Kalina Bontcheva. 2015. USFD: Twitter NER with Drift Compensation and Linked Data.
- Diana Maynard will give a Tutorial on "Practical Sentiment Analysis" at Essex University Summer School on Big Data and Analytics, 24-28 August 2015
- Book chapter publication. Diana Maynard and Jonathon Hare. Entity-based Opinion Mining from Text and Multimedia. In "Advances in Social Media Analysis", Mohamed Gaber, Nirmalie Wiratunga, Ayse Goker, and Mihaela Cocea (eds.) 2015, Springer.
- Diana Maynard gave a keynote speech at 5th International Conference on Web Intelligence, Mining and Semantics (WIMS), July 13-15, 2015, Cyprus. "What you Tweet is What You Get: challenges and opportunities for social media analysis in industry"
- The annual GATE training course was held in Sheffield from 8-12 June, with 21 participants.
- Diana Maynard gave a tutorial on "Text Analysis with GATE" at the Reading University Workshop on Big Social Data, 24 April 2015.
- A paper by Roland Roller and Mark Stevenson (Self-supervised Relation Extraction using UMLS) won the best paper award atCLEF 2014
- Paper published in the Journal of Biomedical Informatics: B. McInnes and M. Stevenson (2014) Determining the Difficulty of Word Sense Disambiguation. Journal of Biomedical Informatics, 47:83-90.
- Paper accepted for the journal Studies in the Digital Humanities: M. Hall, P. Goodale, P. Clough and M. Stevenson (2014) The PATHS System for Exploring Digital Cultural Heritage. Studies in the Digital Humanities.
- Paper published in the journal Information Retrieval: M. Hall, S. Fernando, P. Clough, A. Soroa, E. Agirre and M. Stevenson (2014) Evaluating hierarchical organisation structures for exploring digital libraries. Information Retrieval 17(4):351-379.
- Paper accepted for the journal Science of Computer Programming M. Shahbaz, P. McMinn and M. Stevenson (2014) Automatic generation of valid and invalid test data for string validation routines using web searches and regular expressions. Science of Computer Programming.
- Paper from the group published at ACL 2014: N. Aletras and M. Stevenson (2014) Labelling Topics using Unsupervised Graph-based Methods. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), pages 631--636, Baltimore, Maryland
- Paper from the group published at Digital Libraries 2014: N. Aletras, T. Baldwin, J. Lau and M. Stevenson (2014) Representing Topics Labels for Exploring Digital Libraries. In Digital Libraries 2014 (ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014) and International Conference on Theory and Practice of Digital Libraries (TPDL 2014), London, UK
- Paper from the group published at EACL 2014: N. Aletras and M. Stevenson (2014) Measuring the Similarity between Automatically Generated Topics. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 22--27, Gothenburg, Sweden
Papers from the group published at EMNLP 2014:
- Wilker Aziz and Lucia Specia. 2014. Exact Decoding for Phrase-Based Statistical Machine Translation. EMNLP, Doha.
- Daniel Beck, Trevor Cohn and Lucia Specia. 2014. Joint Emotion Analysis via Multi-task Gaussian Processes. EMNLP, Doha.
- Kashif Shah, Trevor Cohn and Lucia Specia. 2014. A Bayesian non-Linear Method for Feature Selection in Machine Translation Quality Estimation. Machine Translation.
- The University of Sheffield (Sheffield NLP Group) was ranked 3rd in the list of institutions that have published the most LREC papers.
- The Clinical TempEval exercise will run at SemEval 2015, a collaboration between researcher at Brandeis University, U. Alabama Birmingham and Leon Derczynski for the University of Sheffield
- Leon Derczynski will give two guest lectures at a course on Network Science and online Social Network Analysis at Uppsala Universitet in May
Members of the group have chapters in 2 new books:
- Documenting Contemporary Society by Preserving Relevant Information from Twitter In 'Twitter and Society', edited by K. Weller, A. Bruns, J. Burgess, M. Mahrt and C. Puschmann, 2014. T. Risse, W. Peters, P. Senellart, D. Maynard
- Crowdsourcing Named Entity Recognition and Entity Linking Corpora in "The Handbook of Linguistic Annotation" edited by Nancy Ide & James Pustejovsky. Kalina Bontcheva, Leon Derczynski, Ian RobertsMatteo Magnani and Leon Derczynski will teach a week-long course at ESSLLI 2014 in Tubingen in August, on "Human Information Networks"
We have 2 demos accepted at EACL 2014:
- The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy Kalina Bontcheva, Ian Roberts and Leon Derczynski
- DKIE: Open Source Information Extraction for Danish Leon Derczynski, Camilla Vilhelmsen Derczynski Field, Kenneth Sejdenfaden Bøgh
The group have 6 papers accepted at LREC 2014
- Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines Marta Sabou, Kalina Bontcheva, Leon Derczynski, Arno Scharl
- An efficient and user-friendly tool for machine translation quality estimation Kashif Shah, Marco Turchi, Lucia Specia
- Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis Diana Maynard
- Bilingual dictionaries for all EU languages, LREC Ahmet Aker, Monica Paramita, Marcis Pinnis, Robert Gaizauskas
- Bootstrapping Term Extractors for Multiple Languages Ahmet Aker, Monica Paramita, Emma Barker, Robert Gaizauskas
- Spatio-temporal grounding of claims made on the web, in Pheme Leon Derczynski, Kalina Bontcheva
- A paper is accepted in JASIST journal: Generating Descriptive Multi-Document Summaries of Geo-Located Entities Using Entity Type Models. JASIST Ahmet Aker, Robert Gaizauskas
- The PHEME project has started. A 3 year EC FP7 project from 1 Jan'14 - 31 Dec'16 with 9 partners worth a total of € 4,269,938 with an EC contribution of € 2,916,000. The University of Sheffield PI is Dr Kalina Bontcheva
Three full papers from the group have been accepted at RANLP 2013, to be held in the spa town of Hisarya, Bulgaria
- "Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data" Derczynski, L., Ritter, A., Clarke, S. & Bontcheva, K.
- "Recognising and Interpreting Named Temporal Expressions" M. Brucato, M., Derczynski, L., Llorens, H., Bontcheva, K. & Jensen, C.S.
- "TwitIE: A Fully-featured Information Extraction Pipeline for Microblog Text" Bontcheva, K., Derczynski, L., Funk, A., Greenwood, M.A., Maynard, D. & Aswani, N.
- The group has had a discussion paper accepted at the International Conference on the Theory of Information Retrieval: "Information Retrieval for Temporal Bounding" Derczynski, L. & Gaizauskas, R.
2 short papers & 3 demonstrations have been accepted by the group at ACL 2013
- "Reducing Annotation Effort for Quality Estimation via Active Learning" Beck, D., Specia, L. & Cohn, T.
- "Temporal Signals Help Label Temporal Relations" Derczynski, L. & Gaizauskas, R.
- "QuEst - A translation quality estimation framework" Specia, L., Shah, K., Guilherme Camargo de Souza, J. & Cohn, T.
- "PATHS: A System for Accessing Cultural Heritage Collections" Agirre, E., Aletras, N., Clough, P., Fernando, S., Goodale, P., Hall, M., Soroa, A. & Stevenson, M.
- "AnnoMarket: An Open Cloud Platform for NLP" Bontcheva, K., Tablan, V., Roberts, I., Cunningham, H. & Dimitrov, M.
- Two out of the three nominations for the ACM SIGWEB Ted Nelson prize at Hypertext 2013, Paris are both from Sheffield's NLP group. (link)
5 papers by the group accepted at ACL 2013
- "Extracting bilingual terminologies from comparable corpora" Aker, A., Paramita, M. & Gaizauskas, R.
- "An Infinite Hierarchical Bayesian Model of Phrasal Translation" Cohn, T. & Haffari, G.
- "Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation" Cohn, T & Specia, L.
- "Markov Translation using Non-parametric Bayesian Inference" Feng, Y. & Cohn, T.
- "A user-centric model of voting intention from Social Media" Lampos, V., Preotiuc-Pietro, D. & Cohn, T.
3 papers by the group accepted for NAACL 2013
- "Representing Topics Using Images" Aletras, N. and Stevenson, M.
- "Unsupervised Domain Tuning to Improve Word Sense Disambiguation" Preiss, J. and Stevenson, M.
- "DALE: A Word Sense Disambiguation System for Biomedical Documents Trained using Automatically Labeled Examples (demo)" Preiss, J. and Stevenson, M.
- The ForgetIT: Concise Preservation by combining Managed Forgetting and Contextualized Remembering project has started. A 3 year EC FP7 project from 1 Feb'13 - 31 Jan'16. The project has 11 partners worth a total of € 9,085,190 with an EC contribution of € 6,590,000. The University of Sheffield PI is Prof. Hamish Cunningham
- The VisualSense: Tagging visual data with semantic descriptions project has started. A 3 year EPSRC project from 1 Jan'13 - 31 Dec'15. The project has 4 partners and is part of the Chist-Era EC funding programme. The University of Sheffield PI is Prof. Rob Gaizauskas
Older news stories
2017 - 2018
7 June 2018 - Peter Cochrane (University of Suffolk) - Self Awareness: The Next BIG Breakthrough in NLP
For >50 years the dream of talking to a machine at a (human) conversational level has always been 30 years in the future. However, recent advances in computer, sensor, network, robotic, and mobile device hardware has brought that horizon much closer. In short; transistor density and connectivity per chip, along with network complexity crossed a critical threshold and accelerated the abilities of AI.
We know that NLP is critically dependent on context and cognition, plus the most vital element - self-awareness; and whilst context is easily established, cognition, and even more so ‘self-awareness’ remain hotly debated and in the future.
Here we present an entropic quantification of AI which is intuitively extended to AL and life in general. Self-awareness is then identified as an emergent property which we attempt to place on a realistic time-line.
22 March 2018 - Marco Damonte (University of Edinburgh) - Natural Language Understanding with Abstract Meaning Representation
Abstract meaning representation (Banarescu et al, 2013), or AMR for short, is a semantic representation that provides sentences with a deep semantic interpretation. AMR includes most of the shallow-semantic NLP tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and coreference resolution. AMR is not an interlingua, but AMR graphs can be exploited for a number of NLP tasks such as machine translation, summarisation and paraphrasing. Text-to-AMR (parsing) and AMR-to-text (generation) is however far from providing and using sufficiently accurate graphs for downstream applications. Moreover, not much work has been carried out on AMR for languages other than English. In this talk I’ll present my work on addressing these issues.
1 March 2018 - Wang Ling (Google DeepMind)
1 February 2018 - Johannes Welbl (University College London) - Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Contemporary Reading Comprehension (RC) datasets — SQuAD, TriviaQA, etc. — are dominated by queries that can be answered with a single paragraph or document. However, enabling models to combine pieces of textual information from different sources would drastically extend the scope of RC. In this talk, I will introduce a novel Multi-hop RC task, where a model has to learn how to find and combine disjoint pieces of textual evidence, effectively performing multi-step (alias multi-hop) inference.
I present two datasets, WikiHop and MedHop, from different domains — both constructed using a unified methodology. I will then discuss the behaviour of several baseline models, including two established end-to-end RC models, BiDAF and FastQA. For example, one model is in fact capable of integrating information across documents, but both models struggle to select relevant information.
Overall the end-to-end models outperform multiple baselines, but their best accuracy is still far behind human performance, leaving ample room for model improvement. It is our hope that these new datasets will drive future RC model development, leading to new and improved applications in areas such as Search, Question Answering, and Fact Checking.
18 January 2018 - Horacio Saggion (Universitat Pompeu Fabra) - Mining and Enriching Scientific Text Collections
In the current online Open Science context, scientific datasets and tools for deep text analysis, visualization and exploitation play a major role. I will present a system developed over the past three years for “deep” analysis and annotation of scientific text collections. After a brief overview of the system and its main components, I will present our current work on the development of a bi-lingual (Spanish and English) fully annotated text resource in the field of natural language processing that we have created with our system. Moreover, a faceted-search and visualization system to explore the created resource will be also discussed.
I will take the opportunity to present further areas of research carried out in our Natural Language Processing group.
7 December 2017 - Miquel Espla-Gomis (Universitat d'Alacant) - Identifying insertion positions in word-level machine translation quality estimation
Machine translation (MT) quality estimation (QE) is the task of predicting the quality of a translation produced by an MT system without having a reference translation. At the level of sentences, quality is usually estimated in terms of the effort required to fix the translation, trying to predict metrics such as translation error rate (TER) or post-editing time. When it comes to word level, QE is usually tackled as the task of identifying which words in the translation need to be replaced or deleted. The main advantage of word-level MT QE in front of MT sentence- or document-level MT QE is that it can be used to help post-editors to focus their attention on those parts of the translation that need to be fixed. However, with the current approach of only identifying the words that need to be fixed, post-editors using word-level MT QE could be disregarding missing words. In order to improve the performance of such systems, we propose an approach capable to identifying both the words that need to be deleted and the positions where one or more words need to be inserted. The work presented compares different types of simple neural network architectures that build on different sources of bilingual information in order to provide such predictions. The results obtained not only confirm the feasibility of the approach proposed, but also that a reasonably high performance on both tasks can be obtained using relatively simple architectures.
16 November 2017 - Zeerak Waseem (The University of Sheffield) - Why the F*ck do You Talk Like That?
Over the past year, abusive language detection has received a surge in interest from the NLP community. In spite of this surge in interest, very little work bases itself in the social scientific theories on abusive language. In addition, little work deals with the social contexts surrounding abusive statements or bridging the gaps that are introduced by switching to different social contexts.
9 November 2017 - Yorick Wilks (University of Sheffield / Florida Institute for Human & Machine Cognition) - Will there be superintelligence and would it hate us?
The paper examines Bostrom’s notion of Superintelligence and argues that, although we should not be sanguine about the future of AI or its potential for harm, superintelligent AI is highly unlikely to come about in the way Bostrom imagines.
2 November 2017 - Emem Rita Usanga (Bnkability) - Rethinking how deals investment is raised in Africa using NLP
With a $100bn annual infrastructure funding deficit over the next 10years and a npopulation anticipated to double by 2045, the need for infrastructure across the African continent is a pressing need. Government acknowledge this can only be done in partnership with private investors. Problem - international private investor often argue there's a lack of bankable projects in Africa.
The issue of bankability, in other words investability, is one that Bnkability aims to solve using NLP to identify and design bankable projects. Some of the key challenges we face include:
- How do you take knowledge based information and turn into data that a machine can assess uploaded project information against?
- How does a machine identify missing elements within a project plan, so not simply a word but sections for instance risk mitigation?
- And even if it is present, how does it assess how robust the risk mitigation strategy is against sector, country and environmental factors?
This is an interactive session where we present our challenges in the application of NLP to our business solution and attendees propose possible solutions.
26 October 2017 - NLP Student Talks
Chiraag Lala - Multimodal Lexical Translation
Inspired by the tasks of Multimodal Machine Translation and Visual Sense Disambiguation we introduce a task called Multimodal Lexical Translation (MLT). The aim of this new task is to correctly translate an ambiguous word given its context - an image and a sentence in the source language. To facilitate the task, we introduce the MLT datasets, where each data point is a $4$-tuple consisting of an ambiguous source word, its visual context (an image), its textual context (a source sentence), and its translation that conforms with the visual and textual contexts. The dataset has been created from the Multi30K corpus using word-alignment followed by human inspection for English to German and English to French language directions. These datasets form a very valuable multimodal and multilingual language resource with several potential uses including evaluation of lexical disambiguation within (Multimodal) Machine Translation systems.
Fernando Manchego - Sentence Simplification via Sequence Labeling
Text Simplification aims to modify the content and structure of a text, in order to make it easier to read and understand. At the sentence-level, several rewriting operations can be performed to achieve this goal: replacing complex words or phrases for simpler synonyms, deleting unimportant content, splitting the sentence, etc. Most research treats sentence simplification as machine translation (MT), with complex and simple as source and target languages, respectively. In this talk, we will first present an in-depth analysis on the potential and limitations of end-to-end MT-style models using automatic and manual evaluations. To deal with some of the identified problems, we devise a two-step sequence labeling method: (i) identify the simplification operations that need to be performed (if any) in each token of sentence, and (ii) execute the operation using transformation-specific strategies. We show that this operation-based approach is able to produce simpler texts than end-to-end models.
19 October 2017 - Kris Cao (University of Cambridge) - Latent variable models of language
Behind the observed surface form of language exist underlying structures and themes, such as syntax, topic and utterance intent. In this talk, I will present some work which composes graphical models to learn underlying variables with powerful data likelihood functions to model the observed surface form. One such application is in open-domain dialogue modelling, where the latent variables capture the variation in the possible responses to a user utterance. We show that the latent variable approach generates more acceptable diverse output, as measured by human annotators. Another is extending topic models to instead learn topics underlying entire sentences, rather than just words. This lets the model learn topics which capture compositional meaning, which a standard word-level model has difficult doing.
12 October 2017 - Sasha Narayan (University of Edinburgh) - Text-to-text Generation Beyond Machine Translation
In recent years we have witnessed the achievements of sequence-to-sequence encoder-decoder models for machine translation.
It is no surprise that these models are also setting a trend in various other generation tasks such as dialogue generation, image caption generation, sentence compression, paraphrase generation, sentence simplification and document summarization. Yet, these deep learning sequence models are often applied off-the-shelf to these text-to-text generation tasks, not tailoring the underlying model to the specific task to improve performance.
In this talk I will discuss two examples, sentence simplification and document summarization, that explore the hypothesis that tailoring the model with knowledge of the task structure and linguistic requirements leads to better performance. In the first part, I will propose a new sentence simplification task (split-and-rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. I will show that the semantically-motivated split model is a key factor in generating fluent and meaning preserving rephrasings.
In the second part, I will discuss the shortcomings of sequence-to-sequence abstractive methods for document summarization and show that an extractive summarization system trained to globally optimize a common summarization evaluation metric outperforms state-of-the-art extractive and abstractive systems in both automatic and extensive human evaluations.
BIO: Shashi Narayan is a postdoctoral researcher in the School of Informatics at the University of Edinburgh. He obtained his PhD in Computer Science at the University of Lorraine, INRIA under Claire Gardent in 2014. His research focuses on natural language generation and understanding with an aim to develop general frameworks for generation from underlying meaning representation or for text rewriting such as summarization, text simplification and paraphrase generation. He also has experience with parsing and other structured prediction problems.
4 September 2017 - Thushari Atapattu (University of Adelaide) - Disclosure Analysis of Educational Big Data
Discourse analysis within the educational context consists of processing natural language data generated from learning and teaching processes including written assessments, transcripts, discussion forums, and micro blogs. Computational approaches for discourse analysis integrates NLP with psychological theories of social interaction, discourse comprehension, and communication. Discourse analysis is a complex problem, particularly within massive classrooms (e.g. Massive Open Online Courses – MOOCs). In this talk, I will discuss two of our research in understanding the academic discourse of lecturers as well as learner-generated discourse in MOOCs. Our work aims to detect the learners’ video interactions patterns and inform us of the influence of quality of lecturers’ discourse. This work analyses millions of video interactions in two MOOCs and found that transition in discourse (i.e. lexical diversity, connectivity) impacts on learners’ video engagement behaviour. Further, I will talk about the association between the quality of learner-generated discourse (i.e. discussion posts) and its impact on learning success. Thus, I will explain how the understanding of discourse enables us to identify the interventions for positive student trajectories.
NLP Reading Group
The target audience is all the members of the NLP group and other possible interested participants.
The meeting will take place weekly for one hour usually on Tuesdays from 11-12pm.
The meetings of the group will be informal and no necessary preparation will be required with the exception of the moderator reading the current paper and the rest having at least a brief overview of it.
Tuesday 12 June 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, Sergey Levine, ICML 2017
Blog post about the paper by the authors
Tuesday 10 April 2018
Style Transfer from Non-Parallel Text by Cross-Alignment
Shen, T; Lei, T; Barzilay, R; Jaakola, T.
Tuesday 3 April 2018
Generating Natural Adversarial Examples
Zhengli Zhao, Dheeru Dua and Sameer Singh
Tuesday 20 February 2018
ACL Paper submission feedback session
Tuesday 13 February 2018
Unbounded cache model for online language modeling with open vocabulary
Edouard Grave, Moustapha Cisse & Armand Joulin
Tuesday 6 February 2018
Neural Sequence Learning Models for Word Sense Disambiguation
Alessandro Raganato, Claudio Delli Bovi & Roberto Navigli
Tuesday 30 January 2018
End-to-End Differentiable Proving
Tim Rocktäschel & Sebastian Riedel
Tuesday 23 January 2018
Unsupervised Learning of Universal Sentence Representations from NLI Data.
Tuesday 28 November 2017
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V. Le, et al.
Tuesday 14 November 2017
Representations of language in a model of visually grounded speech signal
Grzegorz Chrupała, Lieke Gelderloos & Afra Alishahi
Tuesday 7 November 2017
A Class of Submodular Functions for Document Summarization
Hui Lin & Jeff Bilmes
Tuesday 31 October 2017
Question Generation for Question Answering
Nan Duan, Duyu Tang, Peng Chen & Ming Zhou
Tuesday 24 October 2017
Morphological Inflection Generation with Hard Monotonic Attention
Roee Aharoni & Yoav Goldberg
Tuesday 17 October 2017
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
Hao Cheng, Hao Fang, Mari Ostendorf
Tuesday 10 October 2017
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh, Percy Liang; Published in Proceedings of International Conference on Machine Learning, 2017
Tuesday 3 October 2017
Zero-Shot Relation Extraction via Reading Comprehension
Omer Levy, Minjoon Seo, Eunsol Choi and Luke Zettlemoyer
Tuesday 19 September 2017
"Men also like shopping: Reducing Gender Bias Amplification Using Corpus Level Constraints"
Tuesday 29 August 2017
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3309-3318, 2017.
Tuesday 22 August 2017
Split and Rephrase, Accepted for EMNLP 2017
Shashi Narayan, Claire Gardent, Shay B. Cohen and Anastasia Shimorina
Tuesday 15 August 2017
Attention Is All You need
A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely
Tuesday 8 August 2017
Learning to Compute Word Embeddings On the Fly
Dzmitry Bahdanau, Tom Bosc, Stanisław Jastrzębski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio
Tuesday 1 August 2017
Learning to Generate Textual Data, EMNLP 2016
Guillaume Bouchard and Pontus Stenetorp and Sebastian Riedel
Tuesday 11 July 2017
SoundNet: Learning Sound Representations from Unlabeled Video
Yusuf Aytar, Carl Vondrick, Antonio Torralba
Tuesday 4 July 2017
Sentence Simplification with Deep Reinforcement Learning
Xingxing Zhang, Mirella Lapata
Tuesday 27 June 2017
Generation and Comprehension of Unambiguous Object Descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy
Tuesday 20 June 2017
Understanding the BPE algorithm
Tuesday 13 June 2017
Sequence-to-Sequence Models Can Directly Transcribe Foreign Speech
Ron J. Weiss, Jan Chorowski, Navdeep Jaitly, Yonghui Wu, Zhifeng Chen
Tuesday 6 June 2017
Covonlutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
Tuesday 30 May 2017
Program Induction by Rationale Generation:Learning to Solve and Explain Algebraic Word Problems
Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom
Tuesday 9 May 2017
Chatterjee et al.: Online Automatic Post-editing for MT in a Multi-Domain Translation Environment
Tuesday 6 May 2017
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
Tuesday 2 May 2017
Coarse-to-Fine Question Answering for Long Documents
Tuesday 25 April 2017
Re-evaluating Automatic Metrics for Image Captioning
Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, Erkut Erdem
Tuesday 18 April 2017
Neural Tree Indexers, EACL2017
Tuesday 11 April 2017
Tuesday 4 April 2017
Shakir Mohammed's deep learning overview
Tuesday 28 March 2017
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Tuesday 21 March 2017
Unsupervised AMR-Dependency Parse Alignment
Tuesday 14 March 2017
Kim et al. (2016): Examples are not Enough, Learn to Criticize! Criticism for Interpretability, NIPS 2016
Tuesday 7 March 2017
Latent Variable Dialogue Models and their Diversity
Kris Cao and Stephen Clark
Tuesday 28 February 2017
Zhang et al. EACL2017
Tuesday 21 February 2017
Structured Attention Networks
Tuesday 14 February 2017
CORE: Context-Aware Open Relation Extraction with Factorization Machines
by Fabio Petroni, Luciano Del Corro and Rainer Gemulla
Tuesday 7 February 2017
Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato, Andrew, M.Dai, Ian Goodfellow
Tuesday 31 January 2017
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing
Tim Vieira and Jason Eisner
Tuesday 24 January 2017
Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra
Tuesday 17 January 2017
Learning Structured Predictors from Bandit Feedback for Interactive NLP. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL). Berlin, Germany
Artem Sokolov, Julia Kreutzer, Christopher Lo, Stefan Riezler
Tuesday 13 December 2016
Optimization and Sampling for NLP from a Unified Viewpoint
Marc Dymetman, Guillaume Bouchard, Simon Carter
Tuesday 6 December 2016
Matrix Completion has No Spurious Local Minimum
Rong Ge, Jason D. Lee, Tengyu Ma
Tuesday 29 November 2016
Compositional Semantic Parsing on Semi-Structured Tables
Panupong Pasupat and Percy Liang
Tuesday 22 November 2016
Minimum Risk Training for Neural Machine Translation
Shiqi Shen, Yong Cheng, Zhougjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu
Tuesday 15 November 2016
Generation from Abstract Meaning Representation using Tree Transducers
Jeffrey Flanigan, Chris Dyer, Noah A. Smith and Jaime Carbonell
Tuesday 1 November 2016
Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels Transactions of the Association for Computational Linguistics, 2016.
Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Leah Findlater, Jordan Boyd-Graber, and Niklas Elmqvist
Tuesday 25 October 2016
Learning to Search Better than your Teacher
Chang et al. ICML 2015
Tuesday 11 October 2016
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Danqi Chen, Jason Bolton, Christopher D. Manning
Tuesday 4 October 2016
Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe, Sebastian Ebert, Hinrich Schütze
Tuesday 7 June 2016
Not All Character N-grams Are Created Equal: A Study in Authorship Attribution.
Upendra Sapkota, Steven Bethard, Manuel Montes-y-Gómez & Thamar Solorio (2015)
Tuesday 31 May 2016
Relation extraction with matrix factorization and universal schemas.
Riedel, S., Yao, L., McCallum, A., & Marlin, B. M. (2013)
Tuesday 10 May 2016
Training Deterministic Parsers with Non-Deterministic Oracles, TACL
Goldberg, Y. and Nivre, J. (2013)
Tuesday 3 May 2016
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing
Vlachos, A. and Clark, S.
Tuesday 22 April 2016
Sequence Level Training with recurrent Neural Networks
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
Tuesday 22 March 2016
"Distributed Representation of Sentences and Documents"
Quoc Le and Tomas Mikolov
Tuesday 8 March 2016
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
Sascha Rothe; Hinrich Schütze. ACL2015 (best student paper)
Tuesday 23 February 2016
From Word Embeddings To Document Distances
Kusner et al.
Tuesday 16 February 2016
"Target-Dependent Twitter Sentiment Classification with Rich Automatic Features"
Tuesday 9 February 2016
"Evaluation methods for unsupervised word embeddings"
Tuesday 25 January 2016
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks
Hua He, Kevin Gimpel, and Jimmy Lin. EMNLP2015
Tuesday 19 January 2016
Multilingual Image Description with Neural Sequence Models
Tuesday 12 January 2016
"Improving Distributional Similarity with Lessons Learned from Word Embeddings"
Tuesday 8 December 2015
Using Discourse Structure Improves Machine Translation Evaluation.
F Guzmán, S Joty, L Màrquez, P Nakov
And here are the author's slides
Tuesday 1 December 2015
Practical Bayesian Optimization of Machine Learning Algorithms Advances in Neural Information Processing Systems, 2012
Snoek, J.; Larochelle, H. & Adams, R. P.
Related presentations/lecture slides:
My reading group presentation slides
Tuesday 24 November 2015
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks ACL 2015
LSTMs? Kai Sheng Tai, Richard Socher, Christopher D. Manning
Additional resource about LSTM: "Anyone Can Learn To Code an LSTM-RNN in Python"
Tuesday 17 November 2015
More details on auto encoders for unsupervised pre-training:
Tuesday 10 November 2015
Multi-Metric Optimization Using Ensemble Tuning. NAACL2013. Video
Baskaran Sankaran, Anoop Sarkar and Kevin Duh
Tuesday 3 November 2015
NN tutorials by Quoc Le
Andrej Karpathy's notes
Different objective functions, multiclass problems
Discussion about different activation functions
Tuesday 27 October 2015
Three blog posts introducing RNNs for language modelling in equations and code
might help to read this NLP primer
a thorough explanation of back propagation
Tuesday 20 October 2015
Teaching Machines to Read and Comprehend. NIPS 2015.
Karl Moritz Hermann, Tomáš Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Slides (presented at LXMLS)
NAACL 2013 Tutorial "Deep Learning without Magic"
EMNLP 2014 Tutorial "Embedding Methods for NLP"
Entailment with Neural Attention (better description of attention models than in the NIPS paper in my opinion)
Tuesday 13 October 2015
A large annotated corpus for learning natural language inference. Proceedings of EMNLP 2015.
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning
Should compare this to work on (multilingual) textual similarity